name: Mint Tests

on:
  pull_request:
    branches:
      - master

# This ensures that previous jobs for the PR are canceled when the PR is
# updated.
concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref }}
  cancel-in-progress: true

permissions:
  contents: read

jobs:
  mint-test:
    runs-on: mint
    timeout-minutes: 120
    steps:

export JOB_NAME="$4"
export MINT_MODE="full"

docker system prune -f || true
docker volume prune -f || true
docker volume rm $(docker volume ls -f dangling=true) || true

## change working directory
cd .github/workflows/mint

docker-compose -f minio-${MODE}.yaml up -d
sleep 1m

docker system prune -f || true
docker volume prune -f || true
docker volume rm $(docker volume ls -q -f dangling=true) || true

apiVersion: release-notes/v2
kind: bug-fix
area: traffic-management
issue:
  - 42612
releaseNotes:
  - |

Keith Randall <******@****.***> 1658254141 -0700

Carlos Amedee <******@****.***> 1715265901 -0400

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

ed){var p1=axis.c2p(c1[axis.direction]),p2=axis.c2p(c2[axis.direction]);r[name]={from:Math.min(p1,p2),to:Math.max(p1,p2)}}});return r}function triggerSelectedEvent(){var r=getSelection();plot.getPlaceholder().trigger("plotselected",[r]);if(r.xaxis&&r.yaxis)plot.getPlaceholder().trigger("selected",[{x1:r.xaxis.from,y1:r.yaxis.from,x2:r.xaxis.to,y2:r.yaxis.to}])}function clamp(min,value,max){return value<min?min:value>max?max:value}function setSelectionPos(pos,e){var o=plot.getOptions();var offset...

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

import (
	"database/sql"
	"time"

	"gorm.io/gorm"
)

// User has one `Account` (has one), many `Pets` (has many) and `Toys` (has many - polymorphic)
// He works in a Company (belongs to), he has a Manager (belongs to - single-table), and also managed a Team (has many - single-table)
// He speaks many languages (many to many) and has many friends (many to many - single-table)
// His pet also has one Toy (has one - polymorphic)

func f(x int, y uint) {
	if true {
		return "a" > 10 // ERROR "^too many arguments to return$|return with value in function with no return|no result values expected|mismatched types"
	}
	return "gopher" == true, 10 // ERROR "^too many arguments to return$|return with value in function with no return|no result values expected|mismatched types"
}

func main() {
	f(2, 3 < "x", 10) // ERROR "too many arguments|invalid operation|incompatible type"